Portfolio Details

E‑commerce Web Application

This project is a full‑stack e‑commerce web app that lets users browse products, manage a cart, and place orders end‑to‑end. It separates frontend UI, backend APIs and a relational database to simulate a real online store with search, categories, filtering and a simple admin flow.

Tech Stack & Tools

• Frontend: HTML5, CSS3, Bootstrap components, vanilla JavaScript for DOM updates and form validation.
• Backend: Python with Django (or Node.js with Express) exposing REST-style routes for products, cart and orders.
• Database: MySQL tables for users, products, categories, carts, orders and order items with foreign-key relations.
• Other: VS Code, Git & GitHub, browser DevTools for debugging layout and network requests.

Key Features

• Dynamic product listing with category filters and text search.
• Session or token based cart where items can be added, updated and removed without full page reloads.
• User authentication with registration, login and order history views.
• Admin dashboard to create and edit products, update stock and review latest orders.
• Fully responsive pages tested on mobile, tablet and desktop.

What I Learned

• Designing relational schemas for real commerce workflows like inventory and orders.
• Connecting frontend and backend through REST APIs and JSON.
• Organizing reusable UI components and implementing validation on client and server.

Full Stack Open Style Project

This project follows a Full Stack Open approach: a React frontend connected to a Node.js/Express backend API. The focus is on modern single-page app patterns, routing and state management.

Tech Stack & Tools

• Frontend: React functional components with hooks, React Router for navigation.
• Backend: Node.js with Express, structured into routes, controllers and models.
• Database: MongoDB or SQL for persisting users, posts or notes.
• Other: Axios/fetch for HTTP calls, Postman for API testing, GitHub for version control.

Key Features

• Full CRUD operations on a core resource such as notes, contacts or blog posts.
• Client-side routing between list, detail and edit views without page reloads.
• Form validation, loading indicators and error messages for failed API calls.
• Reusable backend middleware for logging, error handling and authentication.

What I Learned

• Designing RESTful endpoints and consuming them from a modern frontend.
• Managing global and local state with React hooks and side-effects.
• Debugging issues that span frontend, backend and database layers.

Portfolio with Bootstrap

A responsive personal portfolio built mainly with Bootstrap and custom CSS to present skills, experience and projects in a clean, recruiter-friendly layout.

Tools & Techniques

• Bootstrap grid system, navbar, cards and utility classes for fast layouting.
• Clear visual hierarchy with hero, about, skills, projects and contact sections.
• Consistent dark theme, typography and spacing for good readability.
• Subtle scroll animations using AOS and optimized WebP images for performance.

What I Learned

• Translating wireframes into a real Bootstrap-based interface.
• Using spacing, color and type to guide a visitor’s attention.
• Balancing animations so the design feels modern but not distracting.

AI‑Powered Chatbot

A conversational assistant that can answer user questions and follow simple flows such as FAQs, guidance and navigation steps.

Tech Stack & Tools

• Python for core logic.
• NLP libraries such as NLTK or spaCy for tokenization, cleaning and intent detection.
• Flask or FastAPI to expose HTTP endpoints or a small web UI.
• JSON or CSV files to store intents, example phrases and responses.

Techniques

• Preprocessing text (lowercasing, stopword removal, stemming/lemmatization).
• Simple intent classification (rule-based or ML-based) to map messages to responses.
• Maintaining session context so the bot can continue earlier conversations.
• Logging user messages for later analysis and improvement.

DSA Patterns & Algorithms Library

A curated collection of data-structure and algorithm implementations in Python, organized by common patterns used in coding interviews.

Content

• Implementations of arrays, linked lists, stacks, queues, hashmaps, trees and graphs.
• Pattern-based problems: sliding window, two pointers, binary search, recursion and DP.
• Multiple solutions (brute-force and optimized) with complexity comparisons.

What I Learned

• Thinking in terms of time and space complexity when comparing solutions.
• Recognizing how similar patterns appear in different problems.
• Writing clean, testable Python code for algorithmic challenges.

Awesome Deep Learning

An exploratory project where different neural-network architectures are trained on image or text datasets to understand deep learning workflows.

Tech Stack

• PyTorch or TensorFlow / Keras for model definition and training.
• Jupyter Notebook or Google Colab for experiments and visualizations.
• Public datasets such as MNIST, CIFAR or text datasets.

Techniques

• Building feed-forward, CNN or simple RNN architectures.
• Using dropout, batch normalization and data augmentation.
• Tracking metrics (loss, accuracy) per epoch and visualizing learning curves.

Anime Recommendation System

A recommendation engine that suggests anime based on user ratings, genres and similarity scores, built as a practical introduction to recommender systems.

Tech Stack & Data

• Python with pandas and NumPy for data cleaning and transformation.
• scikit-learn for similarity metrics and basic recommendation models.
• Public anime rating and metadata datasets (genres, episodes, scores).

Recommendation Methods

• Collaborative filtering based on user-item rating matrices.
• Content-based filtering using genre and metadata similarity between anime.
• Simple hybrid scoring that combines collaborative and content signals.

To‑Do List Automation Tool

A lightweight productivity app that lets users capture tasks, mark them as complete and keep them organized with simple automation rules.

Tech & Features

• HTML, CSS and JavaScript (or React) for an interactive, responsive UI.
• LocalStorage or a small backend API to persist tasks between sessions.
• Create, edit, delete and complete tasks with filters such as All, Active and Completed.
• Optional automation like grouping overdue tasks and showing a daily summary.